78 lines
2.8 KiB
Markdown
78 lines
2.8 KiB
Markdown
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---
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license: apache-2.0
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B
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tags:
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- General-Reasoner-7B
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---
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# General-Reasoner: Advancing LLM Reasoning Across All Domains
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<p align="center">
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<a href="https://github.com/TIGER-AI-Lab/General-Reasoner" target="_blank">💻 Code</a> |
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<a href="https://arxiv.org/abs/2505.14652" target="_blank">📄 Paper</a> |
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<a href="https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified" target="_blank">📊 Dataset</a> |
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<a href="https://huggingface.co/collections/TIGER-Lab/general-reasoner-67fe9386e43e046489eac013" target="_blank">🤗 Model</a> |
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<a href="https://tiger-ai-lab.github.io/General-Reasoner/" target="_blank">🌐 Project Page</a>
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</p>
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## Overview
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<p align="center">
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<img src="https://tiger-ai-lab.github.io/General-Reasoner/static/images/teaser.png" alt="General-Reasoner Teaser" width="650"/>
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</p>
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<p align="center" style="font-style: italic; font-size: 0.95rem;">
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<em>
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Figure: Effectiveness of <strong>General-Reasoner</strong> trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks.
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</em>
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</p>
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**General-Reasoner** is a training paradigm for large language models (LLMs), designed to robustly enhance reasoning abilities across diverse domains—not just mathematics and coding, but also physics, chemistry, finance, humanities, and more.
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**Key features:**
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- **Zero RL Training:** Direct reinforcement learning from base LLMs, bypassing intermediate supervised stages.
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- **Diverse Reasoning Data:** 230K+ high-quality, verifiable questions sourced from the web and filtered for answer verifiability across disciplines.
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- **Model-Based Verifier:** Compact 1.5B generative verifier model for context-aware, chain-of-thought answer validation, outperforming traditional rule-based methods.
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**This specific model is the General-Reasoner variant trained based on [Qwen2.5-7B-Base](https://huggingface.co/Qwen/Qwen2.5-7B).**
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## Main Results
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General-Reasoner outperforms base and supervised models on a variety of reasoning benchmarks, demonstrating robust generalization across domains:
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<p align="center">
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<a href="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" target="_blank">
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<img src="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" alt="Main Results" width="600">
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</a>
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</p>
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## Citation
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If you feel our work is helpful, please cite:
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```bibtex
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@article{general-reasoner,
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title={{G}eneral-{R}easoner: Advancing LLM Reasoning Across All Domains},
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author={Xueguang Ma and Qian Liu and Dongfu Jiang and Ge Zhang and Zejun Ma and Wenhu Chen},
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year={2025},
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journal={arXiv:2505.14652},
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url={https://arxiv.org/abs/2505.14652}
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}
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```
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